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Supplemental lighting is necessary for winter greenhouse tomato production, and optimised spectra must consider both yield and energy use. In a 20-week winter greenhouse experiment, two common commercial tomato cultivars – 'Merlice' and 'Brioso' – were grown under four LED spectra with 38%, 63%, 81%, and 95% red photons. As the percentage of red photons increased, the blue and green percentages decreased (not at the same ratio). Stem length, specific leaf area, and dry mass partitioning were not significantly affected by spectra in either cultivar, but increasing the red percentage from 38% to 95% decreased harvested fruit fresh mass by 13%, total plant dry mass by 7.1%, and fruit dry mass by 9.5% in 'Merlice'. There were no significant differences in these parameters for 'Brioso'. The yield kWh−1 increased with increasing percent red in both cultivars because LED fixtures with higher fractions of photons from 660 nm red LEDs have higher photon efficacies (µmol J−1). The efficacies of the lamps in this study were estimated to range from 2.6 to 3.6 µmol J−1. Growers must consider tradeoffs that can occur between yield and efficacy in some cultivars, and how these factors apply to their situation, when choosing the spectrum for their greenhouse. [ABSTRACT FROM AUTHOR]

Intumescence is a physiological disorder in tomato and other plant species that encompasses callus formation on leaves and stems. Next to a genetic predisposition, it has also been shown to be influenced by environmental factors like light spectrum. We grew tomato plants of four different recombinant inbred line (RIL) populations under high‐pressure sodium (HPS) and red/blue LED supplemental lighting in a greenhouse and determined the severity of intumescence on 4‐week‐old plants, in three subsequent replicates. The intumescence severity was scored on a scale from 0 to 3. The severity of intumescence was highly genotype dependent in three out of the four tested tomato populations, with the heritability ranging from 54% to 83%. In those three populations, two to eight QTL for intumescence were identified. One major effect quantitative trait locus (QTL) on the top of chromosome 1 was at a similar position in two genetically different RIL populations. The amount of genetic variation explained for these QTL ranged from 30% to 70% depending on the population. Next to chromosomal influences, we also identified differences in effects from maternal plastids on intumescence, by using reciprocal crosses. The cultivation of the tomato plants under HPS lamps or under red/blue LED supplemental lighting had no significant influence on intumescence score. All major QTLs appeared to be reproducible among the three replicates and among the two light conditions. Significant, though, low negative correlations were identified between the intumescence score and the area of leaves, chlorophyll content index, photosynthesis efficiency and fresh weight to dry weight ratio, which can reflect possible effects of the disorder on multiple aspects of plant performance. [ABSTRACT FROM AUTHOR]

Greenhouse crop production in northern countries often relies heavily on supplemental lighting for year-round yield and product quality. Among the different spectra used in supplemental lighting, red is often considered the most efficient, but plants do not develop normally when grown solely under monochromatic red light ("red light syndrome"). Addition of blue light has been shown to aid normal development, and typical lighting spectra in greenhouse production include a mixture of red and blue light. However, it is unclear whether sunlight, as part of the light available to plants in the greenhouse, may be sufficient as a source of blue light. In a greenhouse high-wire tomato (Solanum lycopersicum), we varied the percentage of blue supplemental light (in a red background) as 0, 6, 12, and 24%, while keeping total photosynthetically active radiation constant. Light was supplied as a mixture of overhead (99 μmol m-2 s-1) and intracanopy (48 μmol m-2 s-1) LEDs, together with sunlight. Averaged over the whole experiment (111 days), sunlight comprised 58% of total light incident onto the crop. Total biomass, yield and number of fruits increased with the addition of blue light to an optimum, suggesting that both low (0%) and high (24%) blue light intensities were suboptimal for growth. Stem and internode lengths, as well as leaf area, decreased with increases in blue light percentage. While photosynthetic capacity increased linearly with increases in blue light percentage, photosynthesis in the low blue light treatment (0%) was not low enough to suggest the occurrence of the red light syndrome. Decreased biomass at low (0%) blue light was likely caused by decreased photosynthetic light use efficiency. Conversely, decreased biomass at high (24%) blue light was likely caused by reductions in canopy light interception. We conclude that while it is not strictly necessary to add blue light to greenhouse supplemental red light to obtain a functional crop, adding some (6–12%) blue light is advantageous for growth and yield while adding 24% blue light is suboptimal for growth. [ABSTRACT FROM AUTHOR]

Background and Aims Plants depend on photosynthesis for growth. In nature, factors such as temperature, humidity, CO2 partial pressure, and spectrum and intensity of irradiance often fluctuate. Whereas irradiance intensity is most influential and has been studied in detail, understanding of interactions with other factors is lacking. Methods We tested how photosynthetic induction after dark-light transitions was affected by CO2 partial pressure (20, 40, 80 Pa), leaf temperatures (15·5, 22·8, 30·5 °C), leaf-to-air vapour pressure deficits (VPDleaf-air; 0·5, 0·8, 1·6, 2·3 kPa) and blue irradiance (0-20 %) in tomato leaves (Solanum lycopersicum). Key Results Rates of photosynthetic induction strongly increased with CO2partial pressure, due to increased apparent Rubisco activation rates and reduced diffusional limitations. High leaf temperature produced slightly higher induction rates, and increased intrinsic water use efficiency and diffusional limitation. High VPDleaf-air slowed down induction rates and apparent Rubisco activation and (at 2·3 kPa) induced damped stomatal oscillations. Blue irradiance had no effect. Slower apparent Rubisco activation in elevated VPDleaf-air may be explained by low leaf internal CO2partial pressure at the beginning of induction. Conclusions The environmental factors CO2 partial pressure, temperature and VPDleaf-air had significant impacts on rates of photosynthetic induction, as well as on underlying diffusional, carboxylation and electron transport processes. Furthermore, maximizing Rubisco activation rates would increase photosynthesis by at most 6-8 % in ambient CO2 partial pressure (across temperatures and humidities), while maximizing rates of stomatal opening would increase photosynthesis by at most 1-3 %. [ABSTRACT FROM AUTHOR]

• Sustainability of four greenhouse production systems is assessed through the lens of SDGs. • Relevant SDGs and suitable indicators are proposed for greenhouse systems. • High-tech greenhouse systems show the best sustainable performance overall. • Organic cultivation in greenhouses shows relatively high nutrient loss, water and land use. • Trade-offs and synergies between SDGs are identified. This review evaluates the sustainability of tomato production in four greenhouse systems: high-tech (The Netherlands) and low-tech (Spain) combined with two ways of cultivation (conventional or organic). The Sustainable Development Goals (SDGs), as defined by the United Nations, were used as a lens to assess the sustainability of these four greenhouse production systems. In total seven SDGs, including 14 targets, were assessed through 12 quantitative and two descriptive indicators. Conventional, high-tech greenhouse systems showed the greatest potential for positive contributions towards four of the SDGs. However, their relatively high energy use makes it difficult to achieve SDG7 on affordable and clean energy, where low-tech systems perform better due to lower energy use from relatively cleaner sources. Lower water use efficiency and higher nutrient losses in all soil-based cultivation systems are barriers to achieving some targets under most of the selected SDGs. Organic cultivation systems showed relatively high water and land use, based on the limited data available. Our review highlights the existence of substantial synergies, but also considerable trade-offs between SDGs. This needs to be considered when making policy, investment and management decisions related to greenhouse production. [ABSTRACT FROM AUTHOR]

This study compared supplemental white light-emitting diode (LED) light provided on top of the canopy (top-light) or partially on top and partially as intracanopy light (ICL) in high-wire cucumber (Cucumis sativus) and tomato (Solanum lycopersicum) crops. The aim was to determine the effects of partially substituting top-light by ICL on fruit yield and its underlying yield components. For each crop, three replicate Venlo glasshouse compartments were used. Two cucumber (HiPower and Skyson) and two tomato cultivars (Brioso and Merlice) were planted in the second half of Oct 2020 and grown on stone wool for a period of 15 weeks (cucumber) or 20 weeks (tomato). Light was supplied at either a light intensity of 250 or 375 mmol.m22.s21, provided either as 100% top-light or as 67% (2/3) top-light and 33% (1/3) ICL. For cucumber at the higher light intensity, 50% more fruits were retained and for tomato at the higher light intensity, planting density was 50% higher to keep the plants balanced in terms of source-to-sink ratio. Substituting 33% of top-light with ICL resulted on average in an increase of 17% in fresh fruit yield for both cucumber and tomato. This increase was twice as high at the higher light intensity (20% to 24%) compared with the lower light intensity (10% to 12%). For both cucumber and tomato, the higher yield for ICL treatments resulted mainly from higher total plant dry weight, whereas partitioning to the fruits was hardly affected. For both crops, the higher plant dry weight resulted from a higher light use efficiency. Increasing light intensity from 250 to 375 mmol.m22.s21 resulted in 38% higher total daily light integral (including solar radiation) and 36% to 37% higher total plant dry weight in cucumber. In tomato, the higher light intensity resulted in 33% higher daily light integral and 36% to 40% total plant dry weight. These values are in agreement with the rule of thumb that 1% increment in light results in 1% increase in plant growth. For cucumber, partially substituting top-light by ICL as well as increasing light intensity resulted in longer and greener fruits, whereas tomato fruit quality (Brix, pH) was unaffected by ICL or light intensity. In conclusion, partially substituting top-light by intracanopy light increased fruit yield and this was even more so at higher than at lower supplemental light intensities. [ABSTRACT FROM AUTHOR]

• Particle filter and DNN increase greenhouse model prediction accuracy. • Calibrating process-based model parameters decreases DNN's need for training data. • The hybrid model retains the interpretability of the process-based model. • The proposed approach makes data-driven boost of model accuracy more interpretable. By generating high quality data without the big time investment and economic cost of real experiments, dynamic greenhouse climate and crop simulation models can support decisions on greenhouse climate control, crop management and greenhouse design. The reliability of simulation-based decisions depends on both the prediction accuracy and interpretability of simulation models. The prediction accuracy of these simulation models can be increased by: 1) improving mechanisms in process-based models; 2) calibrating process-based model parameters; 3) deriving black-box relationships from data. Considering the descending interpretability from (1) to (3), this study presents a knowledge-based data-driven modelling approach where firstly a process-based model is selected and modified based on domain knowledge, then data-driven improvement is applied including two steps: parameter value estimation by particle filter (PF) and further black-box improvement by deep neural networks (DNN). The approach was tested with an example of greenhouse climate-tomato production system modelling. Modules from GreenLight (Katzin et al., 2020) and TOMSIM (Heuvelink, 1995, 1996) were selected, modified and integrated into a process-based greenhouse climate-tomato model. Validation showed that PF-calibration of five greenhouse parameters decreased the seasonal relative root mean squared error (RRMSE) of indoor air vapor pressure predictions from 40.7% of that before PF-calibration to 16.4%, while it did not decrease the RRMSE of indoor air temperature predictions. Combining the PF-calibrated model with a DNN trained on a season of data decreased the RRMSE of indoor air temperature from 15.0% without DNN to 6.7%, and decreased the RRMSE of indoor air vapor pressure to 12.6%. The knowledge-based data-driven greenhouse climate-tomato model had a relative error of 0.9% for seasonal total fresh yield, and an RRMSE of 6.6% for the cumulative yield throughout the season. If process-based model parameters were not calibrated before combining the model with DNNs, the required amount and diversity of DNN training data increased because more information needed to be learnt from data by the DNNs. Without PF-calibration, combining a DNN trained on 50 days of data with the process-based model resulted in RRMSEs of 44.8% and 31.8% for indoor air temperature and vapor pressure prediction, respectively; with PF-calibration, the RRMSEs were decreased to 13.1% and 17.9%. The proposed three-step knowledge-based data-driven approach can not only improve the model prediction accuracy, but can also help to track and interpret the improvements. [ABSTRACT FROM AUTHOR]